trigger CI

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

.github/workflows/self-scheduled.yml

@@ -8,11 +8,9 @@ name: Self-hosted runner (scheduled)
 
 on:
   repository_dispatch:
-  schedule:
-    - cron: "17 2 * * *"
   push:
     branches:
-      - run_scheduled_ci*
+      - run-move-integrations
 
 env:
   HF_HOME: /mnt/cache
@@ -43,7 +41,7 @@ jobs:
     strategy:
       matrix:
         machine_type: [single-gpu, multi-gpu]
-    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
+    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker-past-ci') }}
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -58,7 +56,7 @@ jobs:
     strategy:
       matrix:
         machine_type: [single-gpu, multi-gpu]
-    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
+    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker-past-ci') }}
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -85,7 +83,7 @@ jobs:
         name: Identify models to test
         working-directory: /transformers/tests
         run: |
-          echo "matrix=$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2 + d1; print(d)')" >> $GITHUB_OUTPUT
+          echo "matrix=$(python3 -c 'import os; tests = os.getcwd(); model_tests = os.listdir(os.path.join(tests, "models")); d1 = sorted(list(filter(os.path.isdir, os.listdir(tests)))); d2 = sorted(list(filter(os.path.isdir, [f"models/{x}" for x in model_tests]))); d1.remove("models"); d = d2[:10] + d1; print(d)')" >> $GITHUB_OUTPUT
 
       - name: NVIDIA-SMI
         run: |
@@ -98,7 +96,7 @@ jobs:
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.matrix) }}
         machine_type: [single-gpu]
-    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
+    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker-past-ci') }}
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -159,7 +157,7 @@ jobs:
       matrix:
         folders: ${{ fromJson(needs.setup.outputs.matrix) }}
         machine_type: [multi-gpu]
-    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
+    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker-past-ci') }}
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -219,7 +217,7 @@ jobs:
       fail-fast: false
       matrix:
         machine_type: [single-gpu]
-    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
+    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker-past-ci') }}
     container:
       image: huggingface/transformers-all-latest-gpu
       options: --gpus 0 --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -270,7 +268,7 @@ jobs:
       fail-fast: false
       matrix:
         machine_type: [single-gpu, multi-gpu]
-    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
+    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker-past-ci') }}
     container:
       image: huggingface/transformers-pytorch-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -320,7 +318,7 @@ jobs:
       fail-fast: false
       matrix:
         machine_type: [single-gpu, multi-gpu]
-    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
+    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker-past-ci') }}
     container:
       image: huggingface/transformers-tensorflow-gpu
       options: --gpus all --shm-size "16gb" --ipc host -v /mnt/cache/.cache/huggingface:/mnt/cache/
@@ -371,7 +369,7 @@ jobs:
       fail-fast: false
       matrix:
         machine_type: [single-gpu, multi-gpu]
-    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker') }}
+    runs-on: ${{ format('{0}-{1}', matrix.machine_type, 'docker-past-ci') }}
     needs: setup
     container:
       image: huggingface/transformers-pytorch-deepspeed-latest-gpu

docs/source/en/main_classes/deepspeed.md

@@ -2065,20 +2065,20 @@ In this case you usually need to raise the value of `initial_scale_power`. Setti
 
 ## Non-Trainer Deepspeed Integration
 
-The [`~deepspeed.HfDeepSpeedConfig`] is used to integrate Deepspeed into the 🤗 Transformers core
+The [`~integrations.HfDeepSpeedConfig`] is used to integrate Deepspeed into the 🤗 Transformers core
 functionality, when [`Trainer`] is not used. The only thing that it does is handling Deepspeed ZeRO-3 param gathering and automatically splitting the model onto multiple gpus during `from_pretrained` call. Everything else you have to do by yourself.
 
 When using [`Trainer`] everything is automatically taken care of.
 
 When not using [`Trainer`], to efficiently deploy DeepSpeed ZeRO-3, you must instantiate the
-[`~deepspeed.HfDeepSpeedConfig`] object before instantiating the model and keep that object alive.
+[`~integrations.HfDeepSpeedConfig`] object before instantiating the model and keep that object alive.
 
 If you're using Deepspeed ZeRO-1 or ZeRO-2 you don't need to use `HfDeepSpeedConfig` at all.
 
 For example for a pretrained model:
 
 ```python
-from transformers.deepspeed import HfDeepSpeedConfig
+from transformers.integrations import HfDeepSpeedConfig
 from transformers import AutoModel
 import deepspeed
 
@@ -2092,7 +2092,7 @@ engine = deepspeed.initialize(model=model, config_params=ds_config, ...)
 or for non-pretrained model:
 
 ```python
-from transformers.deepspeed import HfDeepSpeedConfig
+from transformers.integrations import HfDeepSpeedConfig
 from transformers import AutoModel, AutoConfig
 import deepspeed
 
@@ -2108,7 +2108,7 @@ Please note that if you're not using the [`Trainer`] integration, you're complet
 
 ## HfDeepSpeedConfig
 
-[[autodoc]] deepspeed.HfDeepSpeedConfig
+[[autodoc]] integrations.HfDeepSpeedConfig
     - all
 
 ### Custom DeepSpeed ZeRO Inference
@@ -2161,7 +2161,7 @@ Make sure to:
 
 
 from transformers import AutoTokenizer, AutoConfig, AutoModelForSeq2SeqLM
-from transformers.deepspeed import HfDeepSpeedConfig
+from transformers.integrations import HfDeepSpeedConfig
 import deepspeed
 import os
 import torch

examples/research_projects/wav2vec2/test_wav2vec2_deepspeed.py

@@ -32,7 +32,7 @@ from copy import deepcopy  # noqa
 
 from parameterized import parameterized  # noqa
 from transformers import TrainingArguments, is_torch_available  # noqa
-from transformers.deepspeed import is_deepspeed_available  # noqa
+from transformers.integrations.deepspeed import is_deepspeed_available  # noqa
 from transformers.file_utils import WEIGHTS_NAME  # noqa
 from transformers.testing_utils import (  # noqa
     CaptureLogger,

src/transformers/__init__.py

@@ -94,6 +94,7 @@ _import_structure = {
     "data.metrics": [],
     "data.processors": [],
     "debug_utils": [],
+    "deepspeed": [],
     "dependency_versions_check": [],
     "dependency_versions_table": [],
     "dynamic_module_utils": [],
@@ -115,8 +116,6 @@ _import_structure = {
         "is_tensorboard_available",
         "is_wandb_available",
     ],
-    "lib_integrations": [],
-    "lib_integrations.peft": [],
     "modelcard": ["ModelCard"],
     "modeling_tf_pytorch_utils": [
         "convert_tf_weight_name_to_pt_weight_name",
@@ -745,7 +744,6 @@ _import_structure = {
         "is_vision_available",
         "logging",
     ],
-    "utils.bitsandbytes": [],
     "utils.quantization_config": ["BitsAndBytesConfig", "GPTQConfig"],
 }
 
@@ -1002,7 +1000,6 @@ else:
         "TextDataset",
         "TextDatasetForNextSentencePrediction",
     ]
-    _import_structure["deepspeed"] = []
     _import_structure["generation"].extend(
         [
             "BeamScorer",

src/transformers/deepspeed.py

@@ -12,378 +12,29 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 """
-Integration with Deepspeed
+Integration with Deepspeed - kept for backward compatiblity, if you plan to make any edit, make sure to modify the file
+in `integrations/deepspeed` instead.
+
+Check: https://github.com/huggingface/transformers/pull/25599
 """
-
-import importlib.util
-import weakref
-from functools import partialmethod
-
-from .dependency_versions_check import dep_version_check
-from .utils import is_accelerate_available, is_torch_available, logging
-
-
-if is_torch_available():
-    import torch
-
-logger = logging.get_logger(__name__)
-
-
-def is_deepspeed_available():
-    return importlib.util.find_spec("deepspeed") is not None
-
-
-if is_accelerate_available() and is_deepspeed_available():
-    from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
-else:
-    # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
-    # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
-    from builtins import object as DeepSpeedConfig
-
-
-class HfDeepSpeedConfig(DeepSpeedConfig):
-    """
-    This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage.
-
-    A `weakref` of this object is stored in the module's globals to be able to access the config from areas where
-    things like the Trainer object is not available (e.g. `from_pretrained` and `_get_resized_embeddings`). Therefore
-    it's important that this object remains alive while the program is still running.
-
-    [`Trainer`] uses the `HfTrainerDeepSpeedConfig` subclass instead. That subclass has logic to sync the configuration
-    with values of [`TrainingArguments`] by replacing special placeholder values: `"auto"`. Without this special logic
-    the DeepSpeed configuration is not modified in any way.
-
-    Args:
-        config_file_or_dict (`Union[str, Dict]`): path to DeepSpeed config file or dict.
-
-    """
-
-    def __init__(self, config_file_or_dict):
-        # set global weakref object
-        set_hf_deepspeed_config(self)
-        dep_version_check("accelerate")
-        dep_version_check("deepspeed")
-        super().__init__(config_file_or_dict)
-
-
-class HfTrainerDeepSpeedConfig(HfDeepSpeedConfig):
-    """
-    The `HfTrainerDeepSpeedConfig` object is meant to be created during `TrainingArguments` object creation and has the
-    same lifespan as the latter.
-    """
-
-    def __init__(self, config_file_or_dict):
-        super().__init__(config_file_or_dict)
-        self._dtype = None
-        self.mismatches = []
-
-    def dtype(self):
-        if self._dtype is None:
-            raise ValueError("trainer_config_process() wasn't called yet to tell dtype")
-        return self._dtype
-
-    def is_auto(self, ds_key_long):
-        val = self.get_value(ds_key_long)
-        if val is None:
-            return False
-        else:
-            return val == "auto"
-
-    def fill_match(self, ds_key_long, hf_val, hf_key=None, must_match=True):
-        """
-        A utility method that massages the config file and can optionally verify that the values match.
-
-        1. Replace "auto" values with `TrainingArguments` value.
-
-        2. If it wasn't "auto" and `must_match` is true, then check that DS config matches Trainer
-        config values and if mismatched add the entry to `self.mismatched` - will assert during
-        `trainer_config_finalize` for one or more mismatches.
-
-        """
-        config, ds_key = self.find_config_node(ds_key_long)
-        if config is None:
-            return
-
-        if config.get(ds_key) == "auto":
-            config[ds_key] = hf_val
-            return
-
-        if not must_match:
-            return
-
-        ds_val = config.get(ds_key)
-        if ds_val is not None and ds_val != hf_val:
-            self.mismatches.append(f"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}")
-
-    fill_only = partialmethod(fill_match, must_match=False)
-
-    def trainer_config_process(self, args):
-        """
-        Adjust the config with `TrainingArguments` values. This stage is run during `TrainingArguments` object
-        creation.
-        """
-        # DeepSpeed does:
-        # train_batch_size = world_size * train_micro_batch_size_per_gpu * gradient_accumulation_steps
-        train_batch_size = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
-        self.fill_match(
-            "train_micro_batch_size_per_gpu", args.per_device_train_batch_size, "per_device_train_batch_size"
-        )
-        self.fill_match("gradient_accumulation_steps", args.gradient_accumulation_steps, "gradient_accumulation_steps")
-        self.fill_match("train_batch_size", train_batch_size, "train_batch_size (calculated)")
-        self.fill_match("gradient_clipping", args.max_grad_norm, "max_grad_norm")
-
-        self.fill_match("optimizer.params.lr", args.learning_rate, "learning_rate")
-        self.fill_match("optimizer.params.betas", [args.adam_beta1, args.adam_beta2], "adam_beta1+adam_beta2")
-        self.fill_match("optimizer.params.eps", args.adam_epsilon, "adam_epsilon")
-        self.fill_match("optimizer.params.weight_decay", args.weight_decay, "weight_decay")
-
-        self.fill_only("scheduler.params.warmup_min_lr", 0)  # not a trainer arg
-        self.fill_match("scheduler.params.warmup_max_lr", args.learning_rate, "learning_rate")
-        # total_num_steps - will get set in trainer_config_finalize
-
-        # fp16
-        if args.fp16 or args.fp16_full_eval:
-            fp16_backend = "apex" if args.fp16_backend == "apex" else "amp"
-        else:
-            fp16_backend = None
-
-        if args.save_on_each_node:
-            # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
-            self.config["checkpoint"] = self.config.get("checkpoint", {})
-            self.config["checkpoint"]["use_node_local_storage"] = args.save_on_each_node
-
-        # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
-        # any here unless the user did the work
-        self.fill_match(
-            "fp16.enabled",
-            ((args.fp16 or args.fp16_full_eval) and fp16_backend == "amp"),
-            "fp16|fp16_full_eval+fp16_backend(amp)",
-        )
-
-        # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
-        # ZeRO features
-        self.fill_match("amp.enabled", fp16_backend == "apex", "fp16+fp16_backend(apex)")
-        self.fill_match("amp.opt_level", args.fp16_opt_level, "fp16_opt_level")
-
-        self.fill_match("bf16.enabled", (args.bf16 or args.bf16_full_eval), "bf16|bf16_full_eval")
-
-        # deepspeed's default mode is fp16 unless there is a config that says differently
-        if self.is_true("bf16.enabled"):
-            self._dtype = torch.bfloat16
-        elif self.is_false("fp16.enabled"):
-            self._dtype = torch.float32
-        else:
-            self._dtype = torch.float16
-
-    def trainer_config_finalize(self, args, model, num_training_steps):
-        """
-        This stage is run after we have the model and know num_training_steps.
-
-        Now we can complete the configuration process.
-        """
-        # zero
-
-        # deal with config keys that use `auto` value and rely on model's hidden_size
-        hidden_size_based_keys = [
-            "zero_optimization.reduce_bucket_size",
-            "zero_optimization.stage3_prefetch_bucket_size",
-            "zero_optimization.stage3_param_persistence_threshold",
-        ]
-        hidden_size_auto_keys = [x for x in hidden_size_based_keys if self.is_auto(x)]
-
-        if len(hidden_size_auto_keys) > 0:
-            if hasattr(model.config, "hidden_size"):
-                hidden_size = model.config.hidden_size
-            elif hasattr(model.config, "hidden_sizes"):
-                # if there are many hidden sizes pick the largest one
-                hidden_size = max(model.config.hidden_sizes)
-            else:
-                raise ValueError(
-                    "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
-                    "therefore it's not possible to automatically fill out the following `auto` entries "
-                    f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
-                    "`auto` values for these keys with an integer value of your choice."
-                )
-
-            self.fill_only("zero_optimization.reduce_bucket_size", hidden_size * hidden_size)
-            if self.is_zero3():
-                # automatically assign the optimal config values based on model config
-                self.fill_only("zero_optimization.stage3_prefetch_bucket_size", 0.9 * hidden_size * hidden_size)
-                self.fill_only("zero_optimization.stage3_param_persistence_threshold", 10 * hidden_size)
-
-        # scheduler
-        self.fill_match("scheduler.params.total_num_steps", num_training_steps, "num_training_steps (calculated)")
-        self.fill_match("scheduler.params.warmup_num_steps", args.get_warmup_steps(num_training_steps), "warmup_steps")
-
-        if len(self.mismatches) > 0:
-            mismatches = "\n".join(self.mismatches)
-            raise ValueError(
-                "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
-                f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."
-            )
-
-
-# keep the config object global to be able to access it anywhere during TrainingArguments life-cycle
-_hf_deepspeed_config_weak_ref = None
-
-
-def set_hf_deepspeed_config(hf_deepspeed_config_obj):
-    # this is a special weakref global object to allow us to get to Deepspeed config from APIs
-    # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
-    global _hf_deepspeed_config_weak_ref
-    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
-    _hf_deepspeed_config_weak_ref = weakref.ref(hf_deepspeed_config_obj)
-
-
-def unset_hf_deepspeed_config():
-    # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
-    global _hf_deepspeed_config_weak_ref
-    _hf_deepspeed_config_weak_ref = None
-
-
-def is_deepspeed_zero3_enabled():
-    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
-        return _hf_deepspeed_config_weak_ref().is_zero3()
-    else:
-        return False
-
-
-def deepspeed_config():
-    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
-        return _hf_deepspeed_config_weak_ref().config
-    else:
-        return None
-
-
-def deepspeed_optim_sched(trainer, hf_deepspeed_config, args, num_training_steps, model_parameters):
-    """
-    A convenience wrapper that deals with optimizer and lr scheduler configuration.
-    """
-    from accelerate.utils import DummyOptim, DummyScheduler
-
-    config = hf_deepspeed_config.config
-
-    # Optimizer + Scheduler
-    # Currently supported combos:
-    # 1. DS scheduler + DS optimizer: Yes
-    # 2. HF scheduler + HF optimizer: Yes
-    # 3. DS scheduler + HF optimizer: Yes
-    # 4. HF scheduler + DS optimizer: No
-    #
-    # Unless Offload is enabled in which case it's:
-    # 1. DS scheduler + DS optimizer: Yes
-    # 2. HF scheduler + HF optimizer: Mostly*
-    # 3. DS scheduler + HF optimizer: Mostly*
-    # 4. HF scheduler + DS optimizer: No
-    #
-    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)
-
-    optimizer = None
-    if "optimizer" in config:
-        if args.adafactor:
-            raise ValueError(
-                "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
-                "Only one optimizer can be configured."
-            )
-        optimizer = DummyOptim(params=model_parameters)
-    else:
-        if hf_deepspeed_config.is_offload():
-            logger.info(
-                "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
-                " custom optimizer has both CPU and GPU implementation (except LAMB)"
-            )
-
-        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
-        # But trainer uses AdamW by default.
-        optimizer = trainer.create_optimizer()
-        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
-        config["zero_allow_untested_optimizer"] = True
-
-    lr_scheduler = None
-    if "scheduler" in config:
-        lr_scheduler = DummyScheduler(optimizer)
-    else:
-        if isinstance(optimizer, DummyOptim):
-            raise ValueError(
-                "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
-                "Please configure a scheduler in the DeepSpeed config."
-            )
-        lr_scheduler = trainer.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)
-
-    return optimizer, lr_scheduler
-
-
-def deepspeed_init(trainer, num_training_steps, inference=False):
-    """
-    Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args.
-
-    If `resume_from_checkpoint` was passed then an attempt to resume from a previously saved checkpoint will be made.
-
-    Args:
-        trainer: Trainer object
-        num_training_steps: per single gpu
-        resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load
-        inference: launch in inference mode (no optimizer and no lr scheduler)
-
-    Returns: optimizer, lr_scheduler
-
-    We may use `deepspeed_init` more than once during the life of Trainer, when we do - it's a temp hack based on:
-    https://github.com/microsoft/DeepSpeed/issues/1394#issuecomment-937405374 until Deepspeed fixes a bug where it
-    can't resume from a checkpoint after it did some stepping https://github.com/microsoft/DeepSpeed/issues/1612
-
-    """
-    from deepspeed.utils import logger as ds_logger
-
-    model = trainer.model
-    args = trainer.args
-
-    hf_deepspeed_config = trainer.accelerator.state.deepspeed_plugin.hf_ds_config
-
-    # resume config update - some bits like `model` and `num_training_steps` only become available during train
-    hf_deepspeed_config.trainer_config_finalize(args, model, num_training_steps)
-
-    # set the Deepspeed log level consistent with the Trainer
-    ds_logger.setLevel(args.get_process_log_level())
-
-    if inference:
-        # only Z3 makes sense for the inference
-        if not hf_deepspeed_config.is_zero3():
-            raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config")
-
-        # in case the training config is re-used for inference
-        hf_deepspeed_config.del_config_sub_tree("optimizer")
-        hf_deepspeed_config.del_config_sub_tree("lr_scheduler")
-        optimizer, lr_scheduler = None, None
-        model_parameters = None
-    else:
-        trainer.optimizer = None  # important for when deepspeed_init is used as re-init
-        model_parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
-        optimizer, lr_scheduler = deepspeed_optim_sched(
-            trainer, hf_deepspeed_config, args, num_training_steps, model_parameters
-        )
-
-    # keep for quick debug:
-    # from pprint import pprint; pprint(config)
-
-    return optimizer, lr_scheduler
-
-
-def deepspeed_load_checkpoint(deepspeed_engine, checkpoint_path):
-    # it's possible that the user is trying to resume from model_path, which doesn't necessarily
-    # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
-    # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
-    # path contains what looks like a deepspeed checkpoint
-    import glob
-
-    deepspeed_checkpoint_dirs = sorted(glob.glob(f"{checkpoint_path}/global_step*"))
-
-    if len(deepspeed_checkpoint_dirs) > 0:
-        logger.info(f"Attempting to resume from {checkpoint_path}")
-        # this magically updates self.optimizer and self.lr_scheduler
-        load_path, _ = deepspeed_engine.load_checkpoint(
-            checkpoint_path, load_optimizer_states=True, load_lr_scheduler_states=True
-        )
-        if load_path is None:
-            raise ValueError(f"[deepspeed] failed to resume from checkpoint {checkpoint_path}")
-    else:
-        raise ValueError(f"Can't find a valid checkpoint at {checkpoint_path}")
+import warnings
+
+
+warnings.warn(
+    "transformers.deepspeed module is deprecated and will be removed in a future version. Please import deepspeed modules directly from transformers.integrations",
+    FutureWarning,
+)
+
+# Backward compatibility imports, to make sure all those objects can be found in integrations/deepspeed
+from .integrations.deepspeed import (  # noqa
+    HfDeepSpeedConfig,
+    HfTrainerDeepSpeedConfig,
+    deepspeed_config,
+    deepspeed_init,
+    deepspeed_load_checkpoint,
+    deepspeed_optim_sched,
+    is_deepspeed_available,
+    is_deepspeed_zero3_enabled,
+    set_hf_deepspeed_config,
+    unset_hf_deepspeed_config,
+)

src/transformers/generation/utils.py

@@ -24,7 +24,7 @@ import torch
 import torch.distributed as dist
 from torch import nn
 
-from ..deepspeed import is_deepspeed_zero3_enabled
+from ..integrations.deepspeed import is_deepspeed_zero3_enabled
 from ..modeling_outputs import CausalLMOutputWithPast, Seq2SeqLMOutput
 from ..models.auto import (
     MODEL_FOR_CAUSAL_IMAGE_MODELING_MAPPING,

src/transformers/integrations/__init__.py

@@ -0,0 +1,71 @@
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+from .bitsandbytes import (
+    get_keys_to_not_convert,
+    replace_8bit_linear,
+    replace_with_bnb_linear,
+    set_module_8bit_tensor_to_device,
+    set_module_quantized_tensor_to_device,
+)
+from .deepspeed import (
+    HfDeepSpeedConfig,
+    HfTrainerDeepSpeedConfig,
+    deepspeed_config,
+    deepspeed_init,
+    deepspeed_load_checkpoint,
+    deepspeed_optim_sched,
+    is_deepspeed_available,
+    is_deepspeed_zero3_enabled,
+    set_hf_deepspeed_config,
+    unset_hf_deepspeed_config,
+)
+from .integration_utils import (
+    INTEGRATION_TO_CALLBACK,
+    AzureMLCallback,
+    ClearMLCallback,
+    CodeCarbonCallback,
+    CometCallback,
+    DagsHubCallback,
+    FlyteCallback,
+    MLflowCallback,
+    NeptuneCallback,
+    NeptuneMissingConfiguration,
+    TensorBoardCallback,
+    WandbCallback,
+    get_available_reporting_integrations,
+    get_reporting_integration_callbacks,
+    hp_params,
+    is_azureml_available,
+    is_clearml_available,
+    is_codecarbon_available,
+    is_comet_available,
+    is_dagshub_available,
+    is_fairscale_available,
+    is_flyte_deck_standard_available,
+    is_flytekit_available,
+    is_mlflow_available,
+    is_neptune_available,
+    is_optuna_available,
+    is_ray_available,
+    is_ray_tune_available,
+    is_sigopt_available,
+    is_tensorboard_available,
+    is_wandb_available,
+    rewrite_logs,
+    run_hp_search_optuna,
+    run_hp_search_ray,
+    run_hp_search_sigopt,
+    run_hp_search_wandb,
+)
+from .peft import PeftAdapterMixin

src/transformers/integrations/bitsandbytes.py

@@ -0,0 +1,290 @@
+import importlib.metadata
+import warnings
+from copy import deepcopy
+
+from packaging import version
+
+from ..utils import is_accelerate_available, is_bitsandbytes_available, logging
+
+
+if is_bitsandbytes_available():
+    import bitsandbytes as bnb
+    import torch
+    import torch.nn as nn
+
+    from ..pytorch_utils import Conv1D
+
+if is_accelerate_available():
+    from accelerate import init_empty_weights
+    from accelerate.utils import find_tied_parameters
+
+logger = logging.get_logger(__name__)
+
+
+def set_module_quantized_tensor_to_device(module, tensor_name, device, value=None, fp16_statistics=None):
+    """
+    A helper function to set a given tensor (parameter of buffer) of a module on a specific device (note that doing
+    `param.to(device)` creates a new tensor not linked to the parameter, which is why we need this function). The
+    function is adapted from `set_module_tensor_to_device` function from accelerate that is adapted to support the
+    class `Int8Params` from `bitsandbytes`.
+
+    Args:
+        module (`torch.nn.Module`):
+            The module in which the tensor we want to move lives.
+        tensor_name (`str`):
+            The full name of the parameter/buffer.
+        device (`int`, `str` or `torch.device`):
+            The device on which to set the tensor.
+        value (`torch.Tensor`, *optional*):
+            The value of the tensor (useful when going from the meta device to any other device).
+        fp16_statistics (`torch.HalfTensor`, *optional*):
+            The list of fp16 statistics to set on the module, used for serialization.
+    """
+    # Recurse if needed
+    if "." in tensor_name:
+        splits = tensor_name.split(".")
+        for split in splits[:-1]:
+            new_module = getattr(module, split)
+            if new_module is None:
+                raise ValueError(f"{module} has no attribute {split}.")
+            module = new_module
+        tensor_name = splits[-1]
+
+    if tensor_name not in module._parameters and tensor_name not in module._buffers:
+        raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.")
+    is_buffer = tensor_name in module._buffers
+    old_value = getattr(module, tensor_name)
+
+    if old_value.device == torch.device("meta") and device not in ["meta", torch.device("meta")] and value is None:
+        raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {device}.")
+
+    is_4bit = False
+    is_8bit = False
+    if is_buffer or not is_bitsandbytes_available():
+        is_8bit = False
+        is_4bit = False
+    else:
+        is_4bit = hasattr(bnb.nn, "Params4bit") and isinstance(module._parameters[tensor_name], bnb.nn.Params4bit)
+        is_8bit = isinstance(module._parameters[tensor_name], bnb.nn.Int8Params)
+
+    if is_8bit or is_4bit:
+        param = module._parameters[tensor_name]
+        if param.device.type != "cuda":
+            if value is None:
+                new_value = old_value.to(device)
+            elif isinstance(value, torch.Tensor):
+                new_value = value.to("cpu")
+                if value.dtype == torch.int8:
+                    is_8bit_serializable = version.parse(importlib.metadata.version("bitsandbytes")) > version.parse(
+                        "0.37.2"
+                    )
+                    if not is_8bit_serializable:
+                        raise ValueError(
+                            "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
+                            "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."
+                        )
+            else:
+                new_value = torch.tensor(value, device="cpu")
+
+            # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
+            # Since weights are saved in the correct "orientation", we skip transposing when loading.
+            if issubclass(module.source_cls, Conv1D) and fp16_statistics is None:
+                new_value = new_value.T
+
+            kwargs = old_value.__dict__
+            if is_8bit:
+                new_value = bnb.nn.Int8Params(new_value, requires_grad=False, **kwargs).to(device)
+            elif is_4bit:
+                new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(device)
+
+            module._parameters[tensor_name] = new_value
+            if fp16_statistics is not None:
+                setattr(module.weight, "SCB", fp16_statistics.to(device))
+
+    else:
+        if value is None:
+            new_value = old_value.to(device)
+        elif isinstance(value, torch.Tensor):
+            new_value = value.to(device)
+        else:
+            new_value = torch.tensor(value, device=device)
+
+        if is_buffer:
+            module._buffers[tensor_name] = new_value
+        else:
+            new_value = nn.Parameter(new_value, requires_grad=old_value.requires_grad)
+            module._parameters[tensor_name] = new_value
+
+
+def _replace_with_bnb_linear(
+    model, modules_to_not_convert=None, current_key_name=None, quantization_config=None, has_been_replaced=False
+):
+    """
+    Private method that wraps the recursion for module replacement.
+
+    Returns the converted model and a boolean that indicates if the conversion has been successfull or not.
+    """
+    for name, module in model.named_children():
+        if current_key_name is None:
+            current_key_name = []
+        current_key_name.append(name)
+
+        if (isinstance(module, nn.Linear) or isinstance(module, Conv1D)) and name not in modules_to_not_convert:
+            # Check if the current key is not in the `modules_to_not_convert`
+            if not any(key in ".".join(current_key_name) for key in modules_to_not_convert):
+                with init_empty_weights():
+                    if isinstance(module, Conv1D):
+                        in_features, out_features = module.weight.shape
+                    else:
+                        in_features = module.in_features
+                        out_features = module.out_features
+
+                    if quantization_config.quantization_method() == "llm_int8":
+                        model._modules[name] = bnb.nn.Linear8bitLt(
+                            in_features,
+                            out_features,
+                            module.bias is not None,
+                            has_fp16_weights=quantization_config.llm_int8_has_fp16_weight,
+                            threshold=quantization_config.llm_int8_threshold,
+                        )
+                        has_been_replaced = True
+                    else:
+                        if (
+                            quantization_config.llm_int8_skip_modules is not None
+                            and name in quantization_config.llm_int8_skip_modules
+                        ):
+                            pass
+                        else:
+                            model._modules[name] = bnb.nn.Linear4bit(
+                                in_features,
+                                out_features,
+                                module.bias is not None,
+                                quantization_config.bnb_4bit_compute_dtype,
+                                compress_statistics=quantization_config.bnb_4bit_use_double_quant,
+                                quant_type=quantization_config.bnb_4bit_quant_type,
+                            )
+                            has_been_replaced = True
+                    # Store the module class in case we need to transpose the weight later
+                    model._modules[name].source_cls = type(module)
+                    # Force requires grad to False to avoid unexpected errors
+                    model._modules[name].requires_grad_(False)
+        if len(list(module.children())) > 0:
+            _, has_been_replaced = _replace_with_bnb_linear(
+                module,
+                modules_to_not_convert,
+                current_key_name,
+                quantization_config,
+                has_been_replaced=has_been_replaced,
+            )
+        # Remove the last key for recursion
+        current_key_name.pop(-1)
+    return model, has_been_replaced
+
+
+def replace_with_bnb_linear(model, modules_to_not_convert=None, current_key_name=None, quantization_config=None):
+    """
+    A helper function to replace all `torch.nn.Linear` modules by `bnb.nn.Linear8bit` modules from the `bitsandbytes`
+    library. This will enable running your models using mixed int8 precision as described by the paper `LLM.int8():
+    8-bit Matrix Multiplication for Transformers at Scale`. Make sure `bitsandbytes` compiled with the correct CUDA
+    version of your hardware is installed before running this function. `pip install -i https://test.pypi.org/simple/
+    bitsandbytes`
+
+    The function will be run recursively and replace all `torch.nn.Linear` modules except for the `lm_head` that should
+    be kept as a `torch.nn.Linear` module. The replacement is done under `init_empty_weights` context manager so no
+    CPU/GPU memory is required to run this function. Int8 mixed-precision matrix decomposition works by separating a
+    matrix multiplication into two streams: (1) and systematic feature outlier stream matrix multiplied in fp16
+    (0.01%), (2) a regular stream of int8 matrix multiplication (99.9%). With this method, int8 inference with no
+    predictive degradation is possible for very large models (>=176B parameters).
+
+    Parameters:
+        model (`torch.nn.Module`):
+            Input model or `torch.nn.Module` as the function is run recursively.
+        modules_to_not_convert (`List[`str`]`, *optional*, defaults to `["lm_head"]`):
+            Names of the modules to not convert in `Linear8bitLt`. In practice we keep the `lm_head` in full precision
+            for numerical stability reasons.
+        current_key_name (`List[`str`]`, *optional*):
+            An array to track the current key of the recursion. This is used to check whether the current key (part of
+            it) is not in the list of modules to not convert (for instances modules that are offloaded to `cpu` or
+            `disk`).
+    """
+    modules_to_not_convert = ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
+    model, has_been_replaced = _replace_with_bnb_linear(
+        model, modules_to_not_convert, current_key_name, quantization_config
+    )
+
+    if not has_been_replaced:
+        logger.warning(
+            "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
+            " Please double check your model architecture, or submit an issue on github if you think this is"
+            " a bug."
+        )
+
+    return model
+
+
+# For backward compatibility
+def replace_8bit_linear(*args, **kwargs):
+    warnings.warn(
+        "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead",
+        FutureWarning,
+    )
+    return replace_with_bnb_linear(*args, **kwargs)
+
+
+# For backward compatiblity
+def set_module_8bit_tensor_to_device(*args, **kwargs):
+    warnings.warn(
+        "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead",
+        FutureWarning,
+    )
+    return set_module_quantized_tensor_to_device(*args, **kwargs)
+
+
+def get_keys_to_not_convert(model):
+    r"""
+    An utility function to get the key of the module to keep in full precision if any For example for CausalLM modules
+    we may want to keep the lm_head in full precision for numerical stability reasons. For other architectures, we want
+    to keep the tied weights of the model. The function will return a list of the keys of the modules to not convert in
+    int8.
+
+    Parameters:
+    model (`torch.nn.Module`):
+        Input model
+    """
+    # Create a copy of the model and tie the weights, then
+    # check if it contains tied weights
+    tied_model = deepcopy(model)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
+    tied_model.tie_weights()
+
+    tied_params = find_tied_parameters(tied_model)
+    # For compatibility with Accelerate < 0.18
+    if isinstance(tied_params, dict):
+        tied_keys = sum(list(tied_params.values()), []) + list(tied_params.keys())
+    else:
+        tied_keys = sum(tied_params, [])
+    has_tied_params = len(tied_keys) > 0
+
+    # If there is not tied weights, we want to keep the lm_head（output_embedding) in full precision
+    if not has_tied_params:
+        output_emb = model.get_output_embeddings()
+        if output_emb is not None:
+            list_last_module = [name for name, module in model.named_modules() if id(module) == id(output_emb)]
+            return list_last_module
+
+    # otherwise, no tied weights, no output embedding defined, simply keep the last module in full precision
+    list_modules = list(model.named_parameters())
+    list_last_module = [list_modules[-1][0]]
+    # add last module together with tied weights
+    intersection = set(list_last_module) - set(tied_keys)
+    list_untouched = list(set(tied_keys)) + list(intersection)
+
+    # remove ".weight" from the keys
+    names_to_remove = [".weight", ".bias"]
+    filtered_module_names = []
+    for name in list_untouched:
+        for name_to_remove in names_to_remove:
+            if name_to_remove in name:
+                name = name.replace(name_to_remove, "")
+        filtered_module_names.append(name)
+
+    return filtered_module_names

src/transformers/integrations/deepspeed.py

@@ -0,0 +1,389 @@
+# Copyright 2020 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Integration with Deepspeed
+"""
+
+import importlib.util
+import weakref
+from functools import partialmethod
+
+from ..dependency_versions_check import dep_version_check
+from ..utils import is_accelerate_available, is_torch_available, logging
+
+
+if is_torch_available():
+    import torch
+
+logger = logging.get_logger(__name__)
+
+
+def is_deepspeed_available():
+    return importlib.util.find_spec("deepspeed") is not None
+
+
+if is_accelerate_available() and is_deepspeed_available():
+    from accelerate.utils.deepspeed import HfDeepSpeedConfig as DeepSpeedConfig
+else:
+    # Inherits from a dummy `object` if accelerate is not available, so that python succeeds to import this file.
+    # Deepspeed glue code will never inherit this dummy object as it checks if accelerate is available.
+    from builtins import object as DeepSpeedConfig
+
+
+class HfDeepSpeedConfig(DeepSpeedConfig):
+    """
+    This object contains a DeepSpeed configuration dictionary and can be quickly queried for things like zero stage.
+
+    A `weakref` of this object is stored in the module's globals to be able to access the config from areas where
+    things like the Trainer object is not available (e.g. `from_pretrained` and `_get_resized_embeddings`). Therefore
+    it's important that this object remains alive while the program is still running.
+
+    [`Trainer`] uses the `HfTrainerDeepSpeedConfig` subclass instead. That subclass has logic to sync the configuration
+    with values of [`TrainingArguments`] by replacing special placeholder values: `"auto"`. Without this special logic
+    the DeepSpeed configuration is not modified in any way.
+
+    Args:
+        config_file_or_dict (`Union[str, Dict]`): path to DeepSpeed config file or dict.
+
+    """
+
+    def __init__(self, config_file_or_dict):
+        # set global weakref object
+        set_hf_deepspeed_config(self)
+        dep_version_check("accelerate")
+        dep_version_check("deepspeed")
+        super().__init__(config_file_or_dict)
+
+
+class HfTrainerDeepSpeedConfig(HfDeepSpeedConfig):
+    """
+    The `HfTrainerDeepSpeedConfig` object is meant to be created during `TrainingArguments` object creation and has the
+    same lifespan as the latter.
+    """
+
+    def __init__(self, config_file_or_dict):
+        super().__init__(config_file_or_dict)
+        self._dtype = None
+        self.mismatches = []
+
+    def dtype(self):
+        if self._dtype is None:
+            raise ValueError("trainer_config_process() wasn't called yet to tell dtype")
+        return self._dtype
+
+    def is_auto(self, ds_key_long):
+        val = self.get_value(ds_key_long)
+        if val is None:
+            return False
+        else:
+            return val == "auto"
+
+    def fill_match(self, ds_key_long, hf_val, hf_key=None, must_match=True):
+        """
+        A utility method that massages the config file and can optionally verify that the values match.
+
+        1. Replace "auto" values with `TrainingArguments` value.
+
+        2. If it wasn't "auto" and `must_match` is true, then check that DS config matches Trainer
+        config values and if mismatched add the entry to `self.mismatched` - will assert during
+        `trainer_config_finalize` for one or more mismatches.
+
+        """
+        config, ds_key = self.find_config_node(ds_key_long)
+        if config is None:
+            return
+
+        if config.get(ds_key) == "auto":
+            config[ds_key] = hf_val
+            return
+
+        if not must_match:
+            return
+
+        ds_val = config.get(ds_key)
+        if ds_val is not None and ds_val != hf_val:
+            self.mismatches.append(f"- ds {ds_key_long}={ds_val} vs hf {hf_key}={hf_val}")
+
+    fill_only = partialmethod(fill_match, must_match=False)
+
+    def trainer_config_process(self, args):
+        """
+        Adjust the config with `TrainingArguments` values. This stage is run during `TrainingArguments` object
+        creation.
+        """
+        # DeepSpeed does:
+        # train_batch_size = world_size * train_micro_batch_size_per_gpu * gradient_accumulation_steps
+        train_batch_size = args.world_size * args.per_device_train_batch_size * args.gradient_accumulation_steps
+        self.fill_match(
+            "train_micro_batch_size_per_gpu", args.per_device_train_batch_size, "per_device_train_batch_size"
+        )
+        self.fill_match("gradient_accumulation_steps", args.gradient_accumulation_steps, "gradient_accumulation_steps")
+        self.fill_match("train_batch_size", train_batch_size, "train_batch_size (calculated)")
+        self.fill_match("gradient_clipping", args.max_grad_norm, "max_grad_norm")
+
+        self.fill_match("optimizer.params.lr", args.learning_rate, "learning_rate")
+        self.fill_match("optimizer.params.betas", [args.adam_beta1, args.adam_beta2], "adam_beta1+adam_beta2")
+        self.fill_match("optimizer.params.eps", args.adam_epsilon, "adam_epsilon")
+        self.fill_match("optimizer.params.weight_decay", args.weight_decay, "weight_decay")
+
+        self.fill_only("scheduler.params.warmup_min_lr", 0)  # not a trainer arg
+        self.fill_match("scheduler.params.warmup_max_lr", args.learning_rate, "learning_rate")
+        # total_num_steps - will get set in trainer_config_finalize
+
+        # fp16
+        if args.fp16 or args.fp16_full_eval:
+            fp16_backend = "apex" if args.fp16_backend == "apex" else "amp"
+        else:
+            fp16_backend = None
+
+        if args.save_on_each_node:
+            # deepspeed uses shared storage by default. Let's override this setting if save_on_each_node == True
+            self.config["checkpoint"] = self.config.get("checkpoint", {})
+            self.config["checkpoint"]["use_node_local_storage"] = args.save_on_each_node
+
+        # amp: similar to the pytorch native amp - it has a bunch of optional params but we won't set
+        # any here unless the user did the work
+        self.fill_match(
+            "fp16.enabled",
+            ((args.fp16 or args.fp16_full_eval) and fp16_backend == "amp"),
+            "fp16|fp16_full_eval+fp16_backend(amp)",
+        )
+
+        # apex: delegates amp work to apex (which needs to be available), but it cannot be used with any
+        # ZeRO features
+        self.fill_match("amp.enabled", fp16_backend == "apex", "fp16+fp16_backend(apex)")
+        self.fill_match("amp.opt_level", args.fp16_opt_level, "fp16_opt_level")
+
+        self.fill_match("bf16.enabled", (args.bf16 or args.bf16_full_eval), "bf16|bf16_full_eval")
+
+        # deepspeed's default mode is fp16 unless there is a config that says differently
+        if self.is_true("bf16.enabled"):
+            self._dtype = torch.bfloat16
+        elif self.is_false("fp16.enabled"):
+            self._dtype = torch.float32
+        else:
+            self._dtype = torch.float16
+
+    def trainer_config_finalize(self, args, model, num_training_steps):
+        """
+        This stage is run after we have the model and know num_training_steps.
+
+        Now we can complete the configuration process.
+        """
+        # zero
+
+        # deal with config keys that use `auto` value and rely on model's hidden_size
+        hidden_size_based_keys = [
+            "zero_optimization.reduce_bucket_size",
+            "zero_optimization.stage3_prefetch_bucket_size",
+            "zero_optimization.stage3_param_persistence_threshold",
+        ]
+        hidden_size_auto_keys = [x for x in hidden_size_based_keys if self.is_auto(x)]
+
+        if len(hidden_size_auto_keys) > 0:
+            if hasattr(model.config, "hidden_size"):
+                hidden_size = model.config.hidden_size
+            elif hasattr(model.config, "hidden_sizes"):
+                # if there are many hidden sizes pick the largest one
+                hidden_size = max(model.config.hidden_sizes)
+            else:
+                raise ValueError(
+                    "The model's config file has neither `hidden_size` nor `hidden_sizes` entry, "
+                    "therefore it's not possible to automatically fill out the following `auto` entries "
+                    f"in the DeepSpeed config file: {hidden_size_auto_keys}. You can fix that by replacing "
+                    "`auto` values for these keys with an integer value of your choice."
+                )
+
+            self.fill_only("zero_optimization.reduce_bucket_size", hidden_size * hidden_size)
+            if self.is_zero3():
+                # automatically assign the optimal config values based on model config
+                self.fill_only("zero_optimization.stage3_prefetch_bucket_size", 0.9 * hidden_size * hidden_size)
+                self.fill_only("zero_optimization.stage3_param_persistence_threshold", 10 * hidden_size)
+
+        # scheduler
+        self.fill_match("scheduler.params.total_num_steps", num_training_steps, "num_training_steps (calculated)")
+        self.fill_match("scheduler.params.warmup_num_steps", args.get_warmup_steps(num_training_steps), "warmup_steps")
+
+        if len(self.mismatches) > 0:
+            mismatches = "\n".join(self.mismatches)
+            raise ValueError(
+                "Please correct the following DeepSpeed config values that mismatch TrainingArguments"
+                f" values:\n{mismatches}\nThe easiest method is to set these DeepSpeed config values to 'auto'."
+            )
+
+
+# keep the config object global to be able to access it anywhere during TrainingArguments life-cycle
+_hf_deepspeed_config_weak_ref = None
+
+
+def set_hf_deepspeed_config(hf_deepspeed_config_obj):
+    # this is a special weakref global object to allow us to get to Deepspeed config from APIs
+    # that don't have an easy way to get to the Deepspeed config outside of the Trainer domain.
+    global _hf_deepspeed_config_weak_ref
+    # will go away automatically when HfDeepSpeedConfig is destroyed (when TrainingArguments is destroyed)
+    _hf_deepspeed_config_weak_ref = weakref.ref(hf_deepspeed_config_obj)
+
+
+def unset_hf_deepspeed_config():
+    # useful for unit tests to ensure the global state doesn't leak - call from `tearDown` method
+    global _hf_deepspeed_config_weak_ref
+    _hf_deepspeed_config_weak_ref = None
+
+
+def is_deepspeed_zero3_enabled():
+    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
+        return _hf_deepspeed_config_weak_ref().is_zero3()
+    else:
+        return False
+
+
+def deepspeed_config():
+    if _hf_deepspeed_config_weak_ref is not None and _hf_deepspeed_config_weak_ref() is not None:
+        return _hf_deepspeed_config_weak_ref().config
+    else:
+        return None
+
+
+def deepspeed_optim_sched(trainer, hf_deepspeed_config, args, num_training_steps, model_parameters):
+    """
+    A convenience wrapper that deals with optimizer and lr scheduler configuration.
+    """
+    from accelerate.utils import DummyOptim, DummyScheduler
+
+    config = hf_deepspeed_config.config
+
+    # Optimizer + Scheduler
+    # Currently supported combos:
+    # 1. DS scheduler + DS optimizer: Yes
+    # 2. HF scheduler + HF optimizer: Yes
+    # 3. DS scheduler + HF optimizer: Yes
+    # 4. HF scheduler + DS optimizer: No
+    #
+    # Unless Offload is enabled in which case it's:
+    # 1. DS scheduler + DS optimizer: Yes
+    # 2. HF scheduler + HF optimizer: Mostly*
+    # 3. DS scheduler + HF optimizer: Mostly*
+    # 4. HF scheduler + DS optimizer: No
+    #
+    # Mostly*: All non-native DeepSpeed optimizers that have both CPU and GPU implementation should work (except LAMB)
+
+    optimizer = None
+    if "optimizer" in config:
+        if args.adafactor:
+            raise ValueError(
+                "--adafactor was passed, but also found `optimizer` configured in the DeepSpeed config. "
+                "Only one optimizer can be configured."
+            )
+        optimizer = DummyOptim(params=model_parameters)
+    else:
+        if hf_deepspeed_config.is_offload():
+            logger.info(
+                "Detected ZeRO Offload and non-DeepSpeed optimizers: This combination should work as long as the"
+                " custom optimizer has both CPU and GPU implementation (except LAMB)"
+            )
+
+        # ds supports Adam, OneBitAdam, and Lamb optimizers and can import other optimizers from torch.
+        # But trainer uses AdamW by default.
+        optimizer = trainer.create_optimizer()
+        # To use other optimizers requires voiding warranty with: `zero_allow_untested_optimizer`
+        config["zero_allow_untested_optimizer"] = True
+
+    lr_scheduler = None
+    if "scheduler" in config:
+        lr_scheduler = DummyScheduler(optimizer)
+    else:
+        if isinstance(optimizer, DummyOptim):
+            raise ValueError(
+                "Found `optimizer` configured in the DeepSpeed config, but no `scheduler`. "
+                "Please configure a scheduler in the DeepSpeed config."
+            )
+        lr_scheduler = trainer.create_scheduler(num_training_steps=num_training_steps, optimizer=optimizer)
+
+    return optimizer, lr_scheduler
+
+
+def deepspeed_init(trainer, num_training_steps, inference=False):
+    """
+    Init DeepSpeed, after updating the DeepSpeed configuration with any relevant Trainer's args.
+
+    If `resume_from_checkpoint` was passed then an attempt to resume from a previously saved checkpoint will be made.
+
+    Args:
+        trainer: Trainer object
+        num_training_steps: per single gpu
+        resume_from_checkpoint: path to a checkpoint if to resume from after normal DeepSpeedEngine load
+        inference: launch in inference mode (no optimizer and no lr scheduler)
+
+    Returns: optimizer, lr_scheduler
+
+    We may use `deepspeed_init` more than once during the life of Trainer, when we do - it's a temp hack based on:
+    https://github.com/microsoft/DeepSpeed/issues/1394#issuecomment-937405374 until Deepspeed fixes a bug where it
+    can't resume from a checkpoint after it did some stepping https://github.com/microsoft/DeepSpeed/issues/1612
+
+    """
+    from deepspeed.utils import logger as ds_logger
+
+    model = trainer.model
+    args = trainer.args
+
+    hf_deepspeed_config = trainer.accelerator.state.deepspeed_plugin.hf_ds_config
+
+    # resume config update - some bits like `model` and `num_training_steps` only become available during train
+    hf_deepspeed_config.trainer_config_finalize(args, model, num_training_steps)
+
+    # set the Deepspeed log level consistent with the Trainer
+    ds_logger.setLevel(args.get_process_log_level())
+
+    if inference:
+        # only Z3 makes sense for the inference
+        if not hf_deepspeed_config.is_zero3():
+            raise ValueError("ZeRO inference only makes sense with ZeRO Stage 3 - please adjust your config")
+
+        # in case the training config is re-used for inference
+        hf_deepspeed_config.del_config_sub_tree("optimizer")
+        hf_deepspeed_config.del_config_sub_tree("lr_scheduler")
+        optimizer, lr_scheduler = None, None
+        model_parameters = None
+    else:
+        trainer.optimizer = None  # important for when deepspeed_init is used as re-init
+        model_parameters = list(filter(lambda p: p.requires_grad, model.parameters()))
+        optimizer, lr_scheduler = deepspeed_optim_sched(
+            trainer, hf_deepspeed_config, args, num_training_steps, model_parameters
+        )
+
+    # keep for quick debug:
+    # from pprint import pprint; pprint(config)
+
+    return optimizer, lr_scheduler
+
+
+def deepspeed_load_checkpoint(deepspeed_engine, checkpoint_path):
+    # it's possible that the user is trying to resume from model_path, which doesn't necessarily
+    # contain a deepspeed checkpoint. e.g. examples just check if the dir exists and assume it's
+    # a resume from a checkpoint and not just a local pretrained weight. So we check here if the
+    # path contains what looks like a deepspeed checkpoint
+    import glob
+
+    deepspeed_checkpoint_dirs = sorted(glob.glob(f"{checkpoint_path}/global_step*"))
+
+    if len(deepspeed_checkpoint_dirs) > 0:
+        logger.info(f"Attempting to resume from {checkpoint_path}")
+        # this magically updates self.optimizer and self.lr_scheduler
+        load_path, _ = deepspeed_engine.load_checkpoint(
+            checkpoint_path, load_optimizer_states=True, load_lr_scheduler_states=True
+        )
+        if load_path is None:
+            raise ValueError(f"[deepspeed] failed to resume from checkpoint {checkpoint_path}")
+    else:
+        raise ValueError(f"Can't find a valid checkpoint at {checkpoint_path}")

src/transformers/integrations/integration_utils.py

@@ -30,8 +30,8 @@ from typing import TYPE_CHECKING, Dict, Optional
 
 import numpy as np
 
-from . import __version__ as version
-from .utils import flatten_dict, is_datasets_available, is_pandas_available, is_torch_available, logging
+from .. import __version__ as version
+from ..utils import flatten_dict, is_datasets_available, is_pandas_available, is_torch_available, logging
 
 
 logger = logging.get_logger(__name__)
@@ -68,10 +68,10 @@ if TYPE_CHECKING and _has_neptune:
         except importlib.metadata.PackageNotFoundError:
             _has_neptune = False
 
-from .trainer_callback import ProgressCallback, TrainerCallback  # noqa: E402
-from .trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy  # noqa: E402
-from .training_args import ParallelMode  # noqa: E402
-from .utils import ENV_VARS_TRUE_VALUES, is_torch_tpu_available  # noqa: E402
+from ..trainer_callback import ProgressCallback, TrainerCallback  # noqa: E402
+from ..trainer_utils import PREFIX_CHECKPOINT_DIR, BestRun, IntervalStrategy  # noqa: E402
+from ..training_args import ParallelMode  # noqa: E402
+from ..utils import ENV_VARS_TRUE_VALUES, is_torch_tpu_available  # noqa: E402
 
 
 # Integration functions:

src/transformers/integrations/peft.py

@@ -14,7 +14,7 @@
 import inspect
 from typing import Optional
 
-from ...utils import (
+from ..utils import (
     check_peft_version,
     find_adapter_config_file,
     is_accelerate_available,

src/transformers/lib_integrations/__init__.py

@@ -1,14 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-from .peft import PeftAdapterMixin

src/transformers/lib_integrations/peft/__init__.py

@@ -1,15 +0,0 @@
-# Copyright 2023 The HuggingFace Team. All rights reserved.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-from .peft_mixin import PeftAdapterMixin

src/transformers/modeling_utils.py

@@ -35,10 +35,9 @@ from torch.nn import CrossEntropyLoss
 
 from .activations import get_activation
 from .configuration_utils import PretrainedConfig
-from .deepspeed import deepspeed_config, is_deepspeed_zero3_enabled
 from .dynamic_module_utils import custom_object_save
 from .generation import GenerationConfig, GenerationMixin
-from .lib_integrations import PeftAdapterMixin
+from .integrations import PeftAdapterMixin, deepspeed_config, is_deepspeed_zero3_enabled
 from .pytorch_utils import (  # noqa: F401
     Conv1D,
     apply_chunking_to_forward,
@@ -661,7 +660,7 @@ def _load_state_dict_into_meta_model(
     #   they won't get loaded.
 
     if is_quantized:
-        from .utils.bitsandbytes import set_module_quantized_tensor_to_device
+        from .integrations import set_module_quantized_tensor_to_device
 
     error_msgs = []
 
@@ -2944,7 +2943,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
             keep_in_fp32_modules = []
 
         if load_in_8bit or load_in_4bit:
-            from .utils.bitsandbytes import get_keys_to_not_convert, replace_with_bnb_linear
+            from .integrations import get_keys_to_not_convert, replace_with_bnb_linear
 
             llm_int8_skip_modules = quantization_config.llm_int8_skip_modules
             load_in_8bit_fp32_cpu_offload = quantization_config.llm_int8_enable_fp32_cpu_offload
@@ -3262,7 +3261,7 @@ class PreTrainedModel(nn.Module, ModuleUtilsMixin, GenerationMixin, PushToHubMix
     ):
         is_safetensors = False
         if is_quantized:
-            from .utils.bitsandbytes import set_module_quantized_tensor_to_device
+            from .integrations import set_module_quantized_tensor_to_device
 
         if device_map is not None and "disk" in device_map.values():
             archive_file = (

src/transformers/models/data2vec/modeling_data2vec_audio.py

@@ -25,7 +25,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
     CausalLMOutput,

src/transformers/models/deprecated/mctct/modeling_mctct.py

@@ -23,8 +23,8 @@ import torch.utils.checkpoint
 from torch import nn
 
 from ....activations import ACT2FN
-from ....deepspeed import is_deepspeed_zero3_enabled
 from ....file_utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward
+from ....integrations.deepspeed import is_deepspeed_zero3_enabled
 from ....modeling_outputs import BaseModelOutput, CausalLMOutput
 from ....modeling_utils import (
     PreTrainedModel,

src/transformers/models/distilbert/modeling_distilbert.py

@@ -29,7 +29,7 @@ from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss
 
 from ...activations import get_activation
 from ...configuration_utils import PretrainedConfig
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
     MaskedLMOutput,

src/transformers/models/esm/modeling_esmfold.py

@@ -23,7 +23,7 @@ import torch
 import torch.nn as nn
 from torch.nn import LayerNorm
 
-from ...deepspeed import is_deepspeed_available
+from ...integrations.deepspeed import is_deepspeed_available
 from ...modeling_outputs import ModelOutput
 from ...utils import (
     ContextManagers,

src/transformers/models/fsmt/modeling_fsmt.py

@@ -35,7 +35,7 @@ from torch import Tensor, nn
 from torch.nn import CrossEntropyLoss, LayerNorm
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,

src/transformers/models/hubert/modeling_hubert.py

@@ -24,7 +24,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import (

src/transformers/models/m2m_100/modeling_m2m_100.py

@@ -23,7 +23,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,

src/transformers/models/nllb_moe/modeling_nllb_moe.py

@@ -24,7 +24,7 @@ from torch.nn import CrossEntropyLoss
 from torch.utils.checkpoint import checkpoint
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     MoEModelOutput,
     MoEModelOutputWithPastAndCrossAttentions,

src/transformers/models/sew/modeling_sew.py

@@ -25,7 +25,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import add_code_sample_docstrings, add_start_docstrings, add_start_docstrings_to_model_forward, logging

src/transformers/models/sew_d/modeling_sew_d.py

@@ -26,7 +26,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss, LayerNorm
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput
 from ...modeling_utils import PreTrainedModel
 from ...pytorch_utils import softmax_backward_data

src/transformers/models/speecht5/modeling_speecht5.py

@@ -25,7 +25,7 @@ from torch import nn
 from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, L1Loss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
     BaseModelOutputWithPastAndCrossAttentions,

src/transformers/models/unispeech/modeling_unispeech.py

@@ -26,7 +26,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import BaseModelOutput, CausalLMOutput, SequenceClassifierOutput, Wav2Vec2BaseModelOutput
 from ...modeling_utils import PreTrainedModel
 from ...utils import (

src/transformers/models/unispeech_sat/modeling_unispeech_sat.py

@@ -26,7 +26,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
     CausalLMOutput,

src/transformers/models/wav2vec2/modeling_wav2vec2.py

@@ -26,7 +26,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
     CausalLMOutput,

src/transformers/models/wav2vec2_conformer/modeling_wav2vec2_conformer.py

@@ -25,7 +25,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
     CausalLMOutput,

src/transformers/models/wavlm/modeling_wavlm.py

@@ -26,7 +26,7 @@ from torch import nn
 from torch.nn import CrossEntropyLoss
 
 from ...activations import ACT2FN
-from ...deepspeed import is_deepspeed_zero3_enabled
+from ...integrations.deepspeed import is_deepspeed_zero3_enabled
 from ...modeling_outputs import (
     BaseModelOutput,
     CausalLMOutput,

src/transformers/testing_utils.py

@@ -40,7 +40,6 @@ import requests
 
 from transformers import logging as transformers_logging
 
-from .deepspeed import is_deepspeed_available
 from .integrations import (
     is_clearml_available,
     is_fairscale_available,
@@ -49,6 +48,7 @@ from .integrations import (
     is_sigopt_available,
     is_wandb_available,
 )
+from .integrations.deepspeed import is_deepspeed_available
 from .utils import (
     is_accelerate_available,
     is_apex_available,

src/transformers/trainer.py

@@ -58,9 +58,9 @@ from . import __version__
 from .configuration_utils import PretrainedConfig
 from .data.data_collator import DataCollator, DataCollatorWithPadding, default_data_collator
 from .debug_utils import DebugOption, DebugUnderflowOverflow
-from .deepspeed import deepspeed_init, deepspeed_load_checkpoint
 from .dependency_versions_check import dep_version_check
 from .hyperparameter_search import ALL_HYPERPARAMETER_SEARCH_BACKENDS, default_hp_search_backend
+from .integrations.deepspeed import deepspeed_init, deepspeed_load_checkpoint
 from .modelcard import TrainingSummary
 from .modeling_utils import PreTrainedModel, load_sharded_checkpoint, unwrap_model
 from .models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES, MODEL_MAPPING_NAMES
@@ -1197,7 +1197,7 @@ class Trainer:
             # Rebuild the deepspeed config to reflect the updated training parameters
             from accelerate.utils import DeepSpeedPlugin
 
-            from transformers.deepspeed import HfTrainerDeepSpeedConfig
+            from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
 
             self.args.hf_deepspeed_config = HfTrainerDeepSpeedConfig(self.args.deepspeed)
             self.args.hf_deepspeed_config.trainer_config_process(self.args)
@@ -3899,7 +3899,7 @@ class Trainer:
 
         if self.is_deepspeed_enabled:
             if getattr(self.args, "hf_deepspeed_config", None) is None:
-                from transformers.deepspeed import HfTrainerDeepSpeedConfig
+                from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
 
                 ds_plugin = self.accelerator.state.deepspeed_plugin
 

src/transformers/trainer_pt_utils.py

@@ -35,7 +35,7 @@ from torch import nn
 from torch.utils.data import Dataset, IterableDataset, RandomSampler, Sampler
 from torch.utils.data.distributed import DistributedSampler
 
-from .deepspeed import is_deepspeed_zero3_enabled
+from .integrations.deepspeed import is_deepspeed_zero3_enabled
 from .tokenization_utils_base import BatchEncoding
 from .utils import is_sagemaker_mp_enabled, is_torch_tpu_available, is_training_run_on_sagemaker, logging
 

src/transformers/trainer_seq2seq.py

@@ -20,8 +20,8 @@ import torch
 from torch import nn
 from torch.utils.data import Dataset
 
-from .deepspeed import is_deepspeed_zero3_enabled
 from .generation.configuration_utils import GenerationConfig
+from .integrations.deepspeed import is_deepspeed_zero3_enabled
 from .trainer import Trainer
 from .utils import logging
 

src/transformers/training_args.py

@@ -1647,7 +1647,7 @@ class TrainingArguments:
             # - must be run before the model is created.
             if not is_accelerate_available():
                 raise ValueError("--deepspeed requires Accelerate to be installed: `pip install accelerate`.")
-            from transformers.deepspeed import HfTrainerDeepSpeedConfig
+            from transformers.integrations.deepspeed import HfTrainerDeepSpeedConfig
 
             # will be used later by the Trainer
             # note: leave self.deepspeed unmodified in case a user relies on it not to be modified)

src/transformers/utils/bitsandbytes.py

@@ -1,291 +1,28 @@
-import importlib.metadata
+# Copyright 2023 The HuggingFace Team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
 import warnings
-from copy import deepcopy
-
-from packaging import version
-
-from ..utils import logging
-from .import_utils import is_accelerate_available, is_bitsandbytes_available
 
 
-if is_bitsandbytes_available():
-    import bitsandbytes as bnb
-    import torch
-    import torch.nn as nn
+warnings.warn(
+    "transformers.utils.bitsandbytes module is deprecated and will be removed in a future version. Please import bitsandbytes modules directly from transformers.integrations",
+    FutureWarning,
+)
 
-    from ..pytorch_utils import Conv1D
-
-if is_accelerate_available():
-    from accelerate import init_empty_weights
-    from accelerate.utils import find_tied_parameters
-
-logger = logging.get_logger(__name__)
-
-
-def set_module_quantized_tensor_to_device(module, tensor_name, device, value=None, fp16_statistics=None):
-    """
-    A helper function to set a given tensor (parameter of buffer) of a module on a specific device (note that doing
-    `param.to(device)` creates a new tensor not linked to the parameter, which is why we need this function). The
-    function is adapted from `set_module_tensor_to_device` function from accelerate that is adapted to support the
-    class `Int8Params` from `bitsandbytes`.
-
-    Args:
-        module (`torch.nn.Module`):
-            The module in which the tensor we want to move lives.
-        tensor_name (`str`):
-            The full name of the parameter/buffer.
-        device (`int`, `str` or `torch.device`):
-            The device on which to set the tensor.
-        value (`torch.Tensor`, *optional*):
-            The value of the tensor (useful when going from the meta device to any other device).
-        fp16_statistics (`torch.HalfTensor`, *optional*):
-            The list of fp16 statistics to set on the module, used for serialization.
-    """
-    # Recurse if needed
-    if "." in tensor_name:
-        splits = tensor_name.split(".")
-        for split in splits[:-1]:
-            new_module = getattr(module, split)
-            if new_module is None:
-                raise ValueError(f"{module} has no attribute {split}.")
-            module = new_module
-        tensor_name = splits[-1]
-
-    if tensor_name not in module._parameters and tensor_name not in module._buffers:
-        raise ValueError(f"{module} does not have a parameter or a buffer named {tensor_name}.")
-    is_buffer = tensor_name in module._buffers
-    old_value = getattr(module, tensor_name)
-
-    if old_value.device == torch.device("meta") and device not in ["meta", torch.device("meta")] and value is None:
-        raise ValueError(f"{tensor_name} is on the meta device, we need a `value` to put in on {device}.")
-
-    is_4bit = False
-    is_8bit = False
-    if is_buffer or not is_bitsandbytes_available():
-        is_8bit = False
-        is_4bit = False
-    else:
-        is_4bit = hasattr(bnb.nn, "Params4bit") and isinstance(module._parameters[tensor_name], bnb.nn.Params4bit)
-        is_8bit = isinstance(module._parameters[tensor_name], bnb.nn.Int8Params)
-
-    if is_8bit or is_4bit:
-        param = module._parameters[tensor_name]
-        if param.device.type != "cuda":
-            if value is None:
-                new_value = old_value.to(device)
-            elif isinstance(value, torch.Tensor):
-                new_value = value.to("cpu")
-                if value.dtype == torch.int8:
-                    is_8bit_serializable = version.parse(importlib.metadata.version("bitsandbytes")) > version.parse(
-                        "0.37.2"
-                    )
-                    if not is_8bit_serializable:
-                        raise ValueError(
-                            "Detected int8 weights but the version of bitsandbytes is not compatible with int8 serialization. "
-                            "Make sure to download the latest `bitsandbytes` version. `pip install --upgrade bitsandbytes`."
-                        )
-            else:
-                new_value = torch.tensor(value, device="cpu")
-
-            # Support models using `Conv1D` in place of `nn.Linear` (e.g. gpt2) by transposing the weight matrix prior to quantization.
-            # Since weights are saved in the correct "orientation", we skip transposing when loading.
-            if issubclass(module.source_cls, Conv1D) and fp16_statistics is None:
-                new_value = new_value.T
-
-            kwargs = old_value.__dict__
-            if is_8bit:
-                new_value = bnb.nn.Int8Params(new_value, requires_grad=False, **kwargs).to(device)
-            elif is_4bit:
-                new_value = bnb.nn.Params4bit(new_value, requires_grad=False, **kwargs).to(device)
-
-            module._parameters[tensor_name] = new_value
-            if fp16_statistics is not None:
-                setattr(module.weight, "SCB", fp16_statistics.to(device))
-
-    else:
-        if value is None:
-            new_value = old_value.to(device)
-        elif isinstance(value, torch.Tensor):
-            new_value = value.to(device)
-        else:
-            new_value = torch.tensor(value, device=device)
-
-        if is_buffer:
-            module._buffers[tensor_name] = new_value
-        else:
-            new_value = nn.Parameter(new_value, requires_grad=old_value.requires_grad)
-            module._parameters[tensor_name] = new_value
-
-
-def _replace_with_bnb_linear(
-    model, modules_to_not_convert=None, current_key_name=None, quantization_config=None, has_been_replaced=False
-):
-    """
-    Private method that wraps the recursion for module replacement.
-
-    Returns the converted model and a boolean that indicates if the conversion has been successfull or not.
-    """
-    for name, module in model.named_children():
-        if current_key_name is None:
-            current_key_name = []
-        current_key_name.append(name)
-
-        if (isinstance(module, nn.Linear) or isinstance(module, Conv1D)) and name not in modules_to_not_convert:
-            # Check if the current key is not in the `modules_to_not_convert`
-            if not any(key in ".".join(current_key_name) for key in modules_to_not_convert):
-                with init_empty_weights():
-                    if isinstance(module, Conv1D):
-                        in_features, out_features = module.weight.shape
-                    else:
-                        in_features = module.in_features
-                        out_features = module.out_features
-
-                    if quantization_config.quantization_method() == "llm_int8":
-                        model._modules[name] = bnb.nn.Linear8bitLt(
-                            in_features,
-                            out_features,
-                            module.bias is not None,
-                            has_fp16_weights=quantization_config.llm_int8_has_fp16_weight,
-                            threshold=quantization_config.llm_int8_threshold,
-                        )
-                        has_been_replaced = True
-                    else:
-                        if (
-                            quantization_config.llm_int8_skip_modules is not None
-                            and name in quantization_config.llm_int8_skip_modules
-                        ):
-                            pass
-                        else:
-                            model._modules[name] = bnb.nn.Linear4bit(
-                                in_features,
-                                out_features,
-                                module.bias is not None,
-                                quantization_config.bnb_4bit_compute_dtype,
-                                compress_statistics=quantization_config.bnb_4bit_use_double_quant,
-                                quant_type=quantization_config.bnb_4bit_quant_type,
-                            )
-                            has_been_replaced = True
-                    # Store the module class in case we need to transpose the weight later
-                    model._modules[name].source_cls = type(module)
-                    # Force requires grad to False to avoid unexpected errors
-                    model._modules[name].requires_grad_(False)
-        if len(list(module.children())) > 0:
-            _, has_been_replaced = _replace_with_bnb_linear(
-                module,
-                modules_to_not_convert,
-                current_key_name,
-                quantization_config,
-                has_been_replaced=has_been_replaced,
-            )
-        # Remove the last key for recursion
-        current_key_name.pop(-1)
-    return model, has_been_replaced
-
-
-def replace_with_bnb_linear(model, modules_to_not_convert=None, current_key_name=None, quantization_config=None):
-    """
-    A helper function to replace all `torch.nn.Linear` modules by `bnb.nn.Linear8bit` modules from the `bitsandbytes`
-    library. This will enable running your models using mixed int8 precision as described by the paper `LLM.int8():
-    8-bit Matrix Multiplication for Transformers at Scale`. Make sure `bitsandbytes` compiled with the correct CUDA
-    version of your hardware is installed before running this function. `pip install -i https://test.pypi.org/simple/
-    bitsandbytes`
-
-    The function will be run recursively and replace all `torch.nn.Linear` modules except for the `lm_head` that should
-    be kept as a `torch.nn.Linear` module. The replacement is done under `init_empty_weights` context manager so no
-    CPU/GPU memory is required to run this function. Int8 mixed-precision matrix decomposition works by separating a
-    matrix multiplication into two streams: (1) and systematic feature outlier stream matrix multiplied in fp16
-    (0.01%), (2) a regular stream of int8 matrix multiplication (99.9%). With this method, int8 inference with no
-    predictive degradation is possible for very large models (>=176B parameters).
-
-    Parameters:
-        model (`torch.nn.Module`):
-            Input model or `torch.nn.Module` as the function is run recursively.
-        modules_to_not_convert (`List[`str`]`, *optional*, defaults to `["lm_head"]`):
-            Names of the modules to not convert in `Linear8bitLt`. In practice we keep the `lm_head` in full precision
-            for numerical stability reasons.
-        current_key_name (`List[`str`]`, *optional*):
-            An array to track the current key of the recursion. This is used to check whether the current key (part of
-            it) is not in the list of modules to not convert (for instances modules that are offloaded to `cpu` or
-            `disk`).
-    """
-    modules_to_not_convert = ["lm_head"] if modules_to_not_convert is None else modules_to_not_convert
-    model, has_been_replaced = _replace_with_bnb_linear(
-        model, modules_to_not_convert, current_key_name, quantization_config
-    )
-
-    if not has_been_replaced:
-        logger.warning(
-            "You are loading your model in 8bit or 4bit but no linear modules were found in your model."
-            " Please double check your model architecture, or submit an issue on github if you think this is"
-            " a bug."
-        )
-
-    return model
-
-
-# For backward compatibility
-def replace_8bit_linear(*args, **kwargs):
-    warnings.warn(
-        "`replace_8bit_linear` will be deprecated in a future version, please use `replace_with_bnb_linear` instead",
-        FutureWarning,
-    )
-    return replace_with_bnb_linear(*args, **kwargs)
-
-
-# For backward compatiblity
-def set_module_8bit_tensor_to_device(*args, **kwargs):
-    warnings.warn(
-        "`set_module_8bit_tensor_to_device` will be deprecated in a future version, please use `set_module_quantized_tensor_to_device` instead",
-        FutureWarning,
-    )
-    return set_module_quantized_tensor_to_device(*args, **kwargs)
-
-
-def get_keys_to_not_convert(model):
-    r"""
-    An utility function to get the key of the module to keep in full precision if any For example for CausalLM modules
-    we may want to keep the lm_head in full precision for numerical stability reasons. For other architectures, we want
-    to keep the tied weights of the model. The function will return a list of the keys of the modules to not convert in
-    int8.
-
-    Parameters:
-    model (`torch.nn.Module`):
-        Input model
-    """
-    # Create a copy of the model and tie the weights, then
-    # check if it contains tied weights
-    tied_model = deepcopy(model)  # this has 0 cost since it is done inside `init_empty_weights` context manager`
-    tied_model.tie_weights()
-
-    tied_params = find_tied_parameters(tied_model)
-    # For compatibility with Accelerate < 0.18
-    if isinstance(tied_params, dict):
-        tied_keys = sum(list(tied_params.values()), []) + list(tied_params.keys())
-    else:
-        tied_keys = sum(tied_params, [])
-    has_tied_params = len(tied_keys) > 0
-
-    # If there is not tied weights, we want to keep the lm_head（output_embedding) in full precision
-    if not has_tied_params:
-        output_emb = model.get_output_embeddings()
-        if output_emb is not None:
-            list_last_module = [name for name, module in model.named_modules() if id(module) == id(output_emb)]
-            return list_last_module
-
-    # otherwise, no tied weights, no output embedding defined, simply keep the last module in full precision
-    list_modules = list(model.named_parameters())
-    list_last_module = [list_modules[-1][0]]
-    # add last module together with tied weights
-    intersection = set(list_last_module) - set(tied_keys)
-    list_untouched = list(set(tied_keys)) + list(intersection)
-
-    # remove ".weight" from the keys
-    names_to_remove = [".weight", ".bias"]
-    filtered_module_names = []
-    for name in list_untouched:
-        for name_to_remove in names_to_remove:
-            if name_to_remove in name:
-                name = name.replace(name_to_remove, "")
-        filtered_module_names.append(name)
-
-    return filtered_module_names
+from ..integrations import (  # noqa
+    get_keys_to_not_convert,
+    replace_8bit_linear,
+    replace_with_bnb_linear,
+    set_module_8bit_tensor_to_device,
+    set_module_quantized_tensor_to_device,
+)

tests/deepspeed/test_deepspeed.py

@@ -27,7 +27,11 @@ from parameterized import parameterized
 import tests.trainer.test_trainer
 from tests.trainer.test_trainer import TrainerIntegrationCommon  # noqa
 from transformers import AutoModel, TrainingArguments, is_torch_available, logging
-from transformers.deepspeed import HfDeepSpeedConfig, is_deepspeed_available, unset_hf_deepspeed_config
+from transformers.integrations.deepspeed import (
+    HfDeepSpeedConfig,
+    is_deepspeed_available,
+    unset_hf_deepspeed_config,
+)
 from transformers.testing_utils import (
     CaptureLogger,
     CaptureStd,
@@ -113,7 +117,7 @@ def require_deepspeed_aio(test_case):
 if is_deepspeed_available():
     from deepspeed.utils import logger as deepspeed_logger  # noqa
     from deepspeed.utils.zero_to_fp32 import load_state_dict_from_zero_checkpoint
-    from transformers.deepspeed import deepspeed_config, is_deepspeed_zero3_enabled  # noqa
+    from transformers.integrations.deepspeed import deepspeed_config, is_deepspeed_zero3_enabled  # noqa
 
 
 def get_launcher(distributed=False):

tests/quantization/bnb/test_mixed_int8.py

@@ -131,7 +131,7 @@ class MixedInt8Test(BaseMixedInt8Test):
         from accelerate import init_empty_weights
 
         from transformers import AutoModelForMaskedLM, Blip2ForConditionalGeneration, MptForCausalLM, OPTForCausalLM
-        from transformers.utils.bitsandbytes import get_keys_to_not_convert
+        from transformers.integrations.bitsandbytes import get_keys_to_not_convert
 
         model_id = "mosaicml/mpt-7b"
         config = AutoConfig.from_pretrained(

utils/not_doctested.txt

@@ -383,9 +383,11 @@ src/transformers/hyperparameter_search.py
 src/transformers/image_processing_utils.py
 src/transformers/image_transforms.py
 src/transformers/image_utils.py
-src/transformers/integrations.py
+src/transformers/integrations/bitsandbytes.py
+src/transformers/integrations/deepspeed.py
+src/transformers/integrations/integration_utils.py
+src/transformers/integrations/peft.py
 src/transformers/keras_callbacks.py
-src/transformers/lib_integrations/peft/peft_mixin.py
 src/transformers/modelcard.py
 src/transformers/modeling_flax_outputs.py
 src/transformers/modeling_flax_pytorch_utils.py